Emergence of New Maintenance Methods
Developments in the area of artificial intelligence
(AI) have led to the emergence of expert systems and
neural networks. These solution techniques have
found numerous applications in maintenance planning.
Milacic and Majstorovic 14 report on a survey that
identified a list of 60 different expert maintenance systems
as of 1987. Frequently, the reasons for the use of
expert systems in maintenance are the increasing complexity
of equipment, the interdisciplinary nature of
modern maintenance problems, the departure of maintenance
expertise from an organization due to retirements,
the reduced training time of novice technicians,
and consistently good decisions. ~6 Spur, Specht, and
Gobler 17 discuss two general categories of expert
maintenance systems: associative diagnosis and
model-based diagnosis. In the former, conclusions are
reached based on an analysis of fault possibilities that
are verified by testing. The search tree uses coded
knowledge from domain experts. In the latter, the real
performance of equipment is compared with the simulated
performance of a computer model, and faults
are inferred from the differences between the two.
The applications of expert systems in maintenance
are quite diverse. Representative industries
include automotive, aerospace, electronics, process,
computers, and telecommunications.